Research Scientist, Quantum Chemistry

About us

We're reverse-engineering the origin of life — one of the great unsolved problems in science, and one we think AI finally makes tractable. Understanding this transition, from geochemistry to biochemistry, is what will let us orchestrate molecular networks and build systems that are more capable, adaptive, and efficient.

If we succeed, the applications are vast: catalysis, green synthesis, ab initio synthetic biology, programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet — and let life keep evolving beyond it.

We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We ship fast and iterate quickly. Global team, HQs in Cambridge, MA and London, UK.

The role

You'll turn quantum-chemistry calculations into kinetic datasets and mechanistic insight our ML models can actually train on. You'll study reaction mechanisms across homogeneous, heterogeneous, and enzymatic systems, and build the protocols that make that data reliable at scale.

What you'll do

• Run DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysis

• Build and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantification

• Design kinetic datasets for ML training and validation, and set data-quality standards with ML collaborators

• Extend these methods systematically across catalytic systems and reaction conditions

Essential experience

• PhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanisms

• Fluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)

• Sound DFT judgement for transition-metal systems: functional choice, basis sets, dispersion corrections

• Hands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysis

• Python and the computational-chemistry stack (ASE, cclib, RDKit)

Highly preferred

• First-author work in homogeneous-catalysis kinetics

• Heterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysis

• Advanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approaches

• High-throughput workflows, HPC, and automation

• Uncertainty quantification and protocol benchmarking

• Dataset design and prior collaboration with ML teams

Logistics

We're able to sponsor visas for the right candidate.